Furthermore, although different model layers represent different semantic spaces, the same node in each layer should have similar local structures [1]}.
The previous proposed LSP module [2]} only manually selects one layer output of the teacher and student models to be aligned, which inevitably ignores the information contained in other layers. Therefore, we propose a strategy called Alignahead, where each layer of one student model learns from the next layer of another student model. In this way, the structure information will be spread over all layers of the two student models after several alternating iterations and each layer can capture the local structure information in other layers. We will give a detailed analysis about this phenomenon on section REF . To verify the effectiveness of Alignahead, we conduct experiments on different public datasets and different GNN architectures. The main contributions can be summarized as follows:
